Optimizing LC-MS/MS analysis with DO-MS | Gray Huffman | SCP2019 скачать в хорошем качестве

Optimizing LC-MS/MS analysis with DO-MS | Gray Huffman | SCP2019

Presentation by Gray Huffman at the single-cell proteomics conference https://single-cell.net Playlist of all workshop videos http://workshop2019.single-cell.net. See more at: https://slavovlab.net. Gray Huffman discusses experimental and computational methods for optimizing single-cell mass-spec analysis. The emphasis will be on methods to establish the optimal settings for any specific experiment rather than on reporting a set of settings optimal for all cases. On the experimental side, Gray will discuss experimental standards (samples) and methods that we use to maintain and evaluate nLC cleanliness and performance. On the computational side, we will describe how we use DO-MS to optimize our methods. The performance of ultrasensitive LC-MS/MS methods, such as Single-Cell Proteomics by Mass Spectrometry (SCoPE-MS), depends on multiple interdependent parameters. This interdependence makes it challenging to specifically pinpoint bottlenecks in the LC-MS/MS methods and approaches for resolving them. For example, low signal at MS2 level can be due to poor LC separation, ionization, apex targeting, ion transfer, or ion detection. We sought to specifically diagnose such bottlenecks by interactively visualizing data from all levels of bottom-up LC-MS/MS analysis. Many search engines, such as MaxQuant, already provide such data, and we developed an open source platform for their interactive visualization and analysis: Data-driven Optimization of MS (DO-MS). We found that in many cases DO-MS not only specifically diagnosed bottlenecks but also enabled us to rationally optimize them. For example, we used DO-MS to diagnose poor sampling of the elution peak apex and to optimize it, which increased the efficiency of delivering ions for MS2 analysis by 370 %. DO-MS is easy to install and use, and its GUI allows for interactive data subsetting and high-quality figure generation. The modular design of DO-MS facilitates customization and expansion. DO-MS is available for download from GitHub: https://github.com/SlavovLab/DO-MS Relevant publications for further reading: Huffman RG, Specht H, Chen AT, Slavov N. (2019) DO-MS: Data-Driven Optimization of Mass Spectrometry Methods J. of Proteome Res. DOI: https://doi.org/10.1021/acs.jproteome... Specht H, Emmott E, Koller T, Slavov N (2019) High-throughput single-cell proteomics quantifies the emergence of macrophage heterogeneity, bioRxiv DOI: https://doi.org/10.1101/665307 Specht H, Harmange G, Perlman DH, Emmott E, Niziolek Z, Budnik B, Slavov N. (2018) Automated sample preparation for high-throughput single-cell proteomics bioRxiv DOI: http://dx.doi.org/10.1101/399774 Specht H, and Slavov N. (2018) Transformative opportunities for single cell proteomics Journal of Proteome Research DOI: https://doi.org/10.1021/acs.jproteome... Budnik B., Levy E., Harmange G., Slavov N. (2018) SCoPE-MS: mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation Genome Biology DOI: https://doi.org/10.1186/s13059-018-15...